{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 确定峰宽"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建由钟型曲线之和组成的信号。指定每条曲线的位置、高度和宽度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入依赖库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal as sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,1,1000)\n",
    "Pos = np.array([1,2, 3, 5, 7, 8 ])/10\n",
    "Hgt = np.array([4,4,2,2,2,3])\n",
    "Wdt = np.array([3, 8, 4, 3, 4, 6])/100\n",
    "Gauss = np.zeros((len(x),len(Pos)))\n",
    "for n in range(len(Pos)):\n",
    "    Gauss[:,n] = Hgt[n]*np.exp(  -((x-Pos[n])/Wdt[n])**2 )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制各个单条曲线以及其总和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000,)\n",
      "(1000, 6)\n",
      "(1000,)\n",
      "(1000, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PeakSig = np.zeros((1000,1))\n",
    "for i in range(len(x)):\n",
    "    PeakSig[i] = np.sum(Gauss[i,:])\n",
    "print(x.shape)\n",
    "print(Gauss.shape)\n",
    "print(Gauss[:,0].shape)\n",
    "print(PeakSig.shape)\n",
    "for i in range(len(Pos)):\n",
    "    plt.plot(x,Gauss[:,i],'--')\n",
    "plt.plot(x,PeakSig)\n",
    "plt.yticks(np.arange(0,5.5,0.5))\n",
    "plt.xticks(np.arange(0,1.1,0.1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用sig.find_peaks()来寻找峰值，能够获取波峰的高度、宽度等信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# peaks = sig.find_peaks(np.transpose(PeakSig))\n",
    "# peaks = np.zeros([1,1000])\n",
    "type(PeakSig.transpose())\n",
    "peaks = PeakSig.transpose()\n",
    "peaks = peaks.ravel()\n",
    "PP = peaks.ravel()\n",
    "peaks,pros = sig.find_peaks(peaks,height=1,width=0,prominence=0)\n",
    "left_ips =pros['left_ips']\n",
    "right_ips =pros['right_ips']\n",
    "prominences = pros['prominences']\n",
    "left_bases = pros['left_bases']\n",
    "right_bases = pros['right_bases']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用于修正左右bases\n",
    "def baseCorrect(left_bases,right_bases):\n",
    "    for i in range(len(left_bases)-1):\n",
    "        if(left_bases[i]==left_bases[i+1]):\n",
    "            left_bases[i+1]=right_bases[i]\n",
    "        if (right_bases[i]>left_bases[i+1]):\n",
    "            right_bases[i] = left_bases[i+1]\n",
    "    return left_bases,right_bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2705e196ac8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(PeakSig)\n",
    "plt.grid()\n",
    "plt.plot(peaks,PeakSig[peaks],'x')\n",
    "for i in range(len(peaks)):\n",
    "    # plt.plot([left_ips[i],right_ips[i]],[int(PeakSig[peaks[i]]*0.5),int(PeakSig[peaks[i]]*0.5)])\n",
    "    plt.plot([peaks[i],peaks[i]],[PeakSig[peaks[i]],PeakSig[peaks[i]]-prominences[i]],'r')\n",
    "    plt.plot([left_ips[i],right_ips[i]], [PeakSig[int(left_ips[i])],PeakSig[int(left_ips[i])]], 'b')\n",
    "    # plt.plot([left_bases[i],right_bases[i]], [PeakSig[int(left_bases[i])],PeakSig[int(left_bases[i])]], 'g')\n",
    "plt.legend([\"signal\",\"peak\",\"prominence\",\"width(half-prominence)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda\\envs\\pytorch\\lib\\site-packages\\numpy\\core\\_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2705f24bf60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(PeakSig)\n",
    "plt.grid()\n",
    "plt.plot(peaks,PeakSig[peaks],'x')\n",
    "left_bases,right_bases = baseCorrect(left_bases,right_bases)\n",
    "for i in range(len(peaks)):\n",
    "    # plt.plot([left_ips[i],right_ips[i]],[int(PeakSig[peaks[i]]*0.5),int(PeakSig[peaks[i]]*0.5)])\n",
    "    plt.plot([peaks[i],peaks[i]],[PeakSig[peaks[i]],PeakSig[peaks[i]]-prominences[i]],'r')\n",
    "    plt.plot([left_ips[i],right_ips[i]], [PeakSig[int(left_ips[i])],PeakSig[int(left_ips[i])]], 'b')\n",
    "    plt.plot([right_bases[i],right_bases[i]],[PeakSig[right_bases[i]],0],'black')\n",
    "plt.legend([\"signal\",\"peak\",\"prominence\",\"width(half-prominence)\",\"border\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 75.70645911 168.73448052 274.79960597 474.57563446 690.8414124\n",
      " 676.79108285]\n",
      "[310.55183445 234.20718431 288.70567978 524.42384038 721.57904756\n",
      " 849.04279575]\n",
      "[4.87208499 1.08888111 0.03659839 1.99558959 0.3740642  3.00278824]\n",
      "[  0 145 266 418 586 739]\n",
      "[145 266 418 586 739 999]\n"
     ]
    }
   ],
   "source": [
    "left_ips =pros['left_ips']\n",
    "right_ips =pros['right_ips']\n",
    "prominences = pros['prominences']\n",
    "left_bases = pros['left_bases']\n",
    "right_bases = pros['right_bases']\n",
    "\n",
    "print(left_ips)\n",
    "print(right_ips)\n",
    "print(prominences)\n",
    "print(left_bases)\n",
    "print(right_bases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scipy.signal.peak_widths(x, peaks, rel_height=0.5, prominence_data=None, wlen=None)<br/>\n",
    "\n",
    "x： 序列<br/>\n",
    "带有峰值的信号。\n",
    "\n",
    "peaks： 序列<br/>\n",
    "x 中的峰值索引。\n",
    "\n",
    "rel_height： 浮点数，可选<br/>\n",
    "选择测量峰宽的相对高度，作为其突出度的百分比。 1.0 计算峰在其最低轮廓线的宽度，而 0.5 计算突出高度的一半。必须至少为 0。有关详细说明，请参阅注释。\n",
    "\n",
    "prominence_data： 元组，可选<br/>\n",
    "当使用相同的参数 x 和 peaks 调用时，与 peak_prominences 的输出匹配的三个数组的元组。如果未提供，此数据将在内部计算。\n",
    "\n",
    "wlen： 整数，可选<br/>\n",
    "样本中的窗口长度作为 prominence_data 内部计算的可选参数传递给 peak_prominences。如果给出prominence_data，则忽略此参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda\\envs\\pytorch\\lib\\site-packages\\numpy\\core\\_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2705f2f60b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "half_width,width_heights,half_left_ips,half_right_ips=sig.peak_widths(PP,peaks,rel_height=0.5)\n",
    "\n",
    "plt.plot(PeakSig)\n",
    "plt.grid()\n",
    "plt.plot(peaks,PeakSig[peaks],'x')\n",
    "left_bases,right_bases = baseCorrect(left_bases,right_bases)\n",
    "for i in range(len(peaks)):\n",
    "    # plt.plot([left_ips[i],right_ips[i]],[int(PeakSig[peaks[i]]*0.5),int(PeakSig[peaks[i]]*0.5)])\n",
    "    # plt.plot([peaks[i],peaks[i]],[width_heights[i],0],'r')\n",
    "    plt.plot([half_left_ips[i],half_right_ips[i]], [width_heights[i],width_heights[i]], 'b')\n",
    "    plt.plot([right_bases[i],right_bases[i]],[PeakSig[right_bases[i]],0],'black')\n",
    "plt.legend([\"signal\",\"peak\",\"prominence\",\"width(half-prominence)\",\"border\"])"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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